• Journal of the Optical Society of Korea
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• v.19 no.1
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• pp.95-101
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• 2015

To accurately extract absolute distance information from a self-mixing interferometry (SMI) signal, in this paper we propose an approach based on a particle swarm optimization (PSO) algorithm instead of frequency estimation for absolute distance. The algorithm is utilized to search for the global minimum of the fitness function that is established from the self-mixing signal to find out the actual distance. A resolution superior to $25{\mu}m$ in the range from 3 to 20 cm is obtained by experimental measurement, and the results demonstrate the superiority of the proposed approach in comparison with interpolated FFT. The influence of different external feedback strength parameters and different inertia weights in the algorithm is discussed as well.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.893-900
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• 2019

This paper deals with the analysis of SON (Self Organization Network) technology in LTE networks. SON is a unique LTE feature compared to previous cellular systems UMTS and GSM, and is a cost-effective tool for achieving the best performance in a changing environment. In addition, SON has the function of automating the settings of the network, enabling centralized planning and reducing the need for manual tasks. SON is largely divided into three categories: Self-Configuration, Self-Optimization, and Self-Healing. Each large category has a detailed description, and all the technologies in each category come together to complete the technology called SON. In this paper, we analyzed intensively about ANR among the techniques of Self-Configuration in each of the three categories.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.6
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• pp.1194-1202
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• 2002

A study on the topology optimization of a multi-spectral camera for space-use is presented. The optimization is carried out under self-weight and polishing pressure loading. A multi-spectral camera for space-use experiences degradation of optical image in the space, which can not be detected on the optical test bench on the earth. An optical surface deformation of a primary mirror, which is a principal component of the camera system, is an important factor affecting the optical performance of the whole camera system. In this study, topology optimization of the primary mirror of the camera is presented. As an objective function, a measure of Strehl ratio is used. Total mass of the primary mirror is given as a constraint to the optimization problem. The sensitivities of the objective function and constraint are calculated by direct differentiation method. Optimization procedure is carried out by an optimality criteria method. For the light-weight primary mirror design, a three dimensional model is treated. As a preliminary example, topology optimization considering a self-weight loading is treated. In the second example, the polishing pressure is also included as a loading in the topology optimization of the mirror. Results of the optimized design topology for the mirror with various mass constraints are presented.

• Proceedings of the KSME Conference
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• 2001.06a
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• pp.920-925
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• 2001

A study on the topology optimization of a multi-spectral camera for space-use is presented. A multi-spectral camera for space-use experiences degradation of optical image in the space, which can not be detected on the optical test bench on the earth. An optical surface deformation of a primary mirror, which is a principal component of the camera system, under the self-weight loading is an important factor affecting the optical performance of the whole camera system. In this study, topology optimization of the primary mirror of the camera is presented. Total mass of the primary mirror is given as a constraint to the optimization problem. The sensitivities of the objective function and constraint are calculated by direct differentiation method. Optimization procedure is carried out by an optimality criterion method using the sensitivities of the objective function and the constraint. As a preliminary example, topology optimization considering a self-weight loading is treated. For practical use, the polishing pressure is included as a loading in the topology optimization of the primary mirror. Results of the optimized design topology for the primary mirror with varying mass ratios are presented.

• Electronics and Telecommunications Trends
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• v.35 no.4
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• pp.115-134
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• 2020

Several studies on machine learning (ML) based self-organizing networks (SONs) have been conducted, specifically for LTE, since studies to apply ML to optimize mobile communication systems started with 2G. However, they are still in the infancy stage. Owing to the complicated KPIs and stringent user requirements of 5G, it is necessary to design the 5G SON engine with intelligence to enable users to seamlessly and unlimitedly achieve connectivity regardless of the state of the mobile communication network. Therefore, in this study, we analyze and summarize the current state of machine learning studies applied to SONs as solutions to the complicated optimization problems that are caused by the unpredictable context of mobile communication scenarios.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.322-324
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• 2005

In this paper, we introduce the advanced Self-Organizing Fuzzy Polynomial Neural Network based on optimized FPN by evolutionary algorithm and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed model gives rise to a structurally and parametrically optimized network through an optimal parameters design available within Fuzzy Polynomial Neuron(FPN) by means of GA. Through the consecutive process of such structural and parametric optimization, an optimized and flexible the proposed model is generated in a dynamic fashion. The performance of the proposed model is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models.

• Special Issue of the Society of Naval Architects of Korea
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• 2015.09a
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• pp.41-45
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• 2015

In this study reviewed the validity of the estimated optimum trim by the numerical analysis. For this purpose, the numerical analysis of the trim optimization for 6500TEU container carrier and capesize bulk carrier were carried out using Star-CCM+, which results were compared with the results of model tests. The reliability of results of the numerical analysis was confirmed via comparing the resistance determined by the numerical analysis and model test. The performance of self-propulsion at each trim conditions were estimated using the calculated resistance by numerical analysis. The BHP at each trim condition were calculated by estimated performance of self-propulsion, which trend of results were confirmed similar trend of result of model test.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.3A
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• pp.217-224
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• 2010

In this paper, we propose a hard handover algorithm for a base station's self-optimization, one of the automatic operational technologies for the 3GPP LTE systems. The proposed algorithm simultaneously considers a mixed target sell selection method for optimal selection and a multiple parameter based active hysteresis method with the received signal strength from adjacent cells and the cell load information of the candidate target cells from information exchanges between eNBs through X2 interface. The active hysteresis method chooses optimal handover hysteresis value considering the costs of the various environmental parameters effect to handover performance. The algorithm works on the optimal target cell and the hysteresis value selections for a base station's automatic operational optimization of the LTE system with the gathered informaton effects to the handover performance. The simulation results show distinguished handover performances in terms of the most important performance indexes of handover, handover failure rate and load balancing.

• Computers and Concrete
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• v.17 no.5
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• pp.629-638
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• 2016

Optimization of the concrete mixture design is a process of search for a mixture for which the sum of the cost of the ingredients is the lowest, yet satisfying the required performance of concrete. In this study, a statistical model was carried out to model a cost effective optimal mix proportioning of high strength self-compacting concrete (HSSCC) using the Response Surface Methodology (RSM). The effect of five key mixture parameters such as water-binder ratio, cement content, fine aggregate percentage, fly ash content and superplasticizer content on the properties and performance of HSSCC like compressive strength, passing ability, segregation resistance and manufacturing cost were investigated. To demonstrate the responses of model in quadratic manner Central Composite Design (CCD) was chosen. The statistical model showed the adjusted correlation coefficient R2adj values were 92.55%, 93.49%, 92.33%, and 100% for each performance which establish the adequacy of the model. The optimum combination was determined to be $439.4kg/m^3$ cement content, 35.5% W/B ratio, 50.0% fine aggregate, $49.85kg/m^3$ fly ash, and $7.76kg/m^3$ superplasticizer within the interest region using desirability function. Finally, it is concluded that multiobjective optimization method based on desirability function of the proposed response model offers an efficient approach regarding the HSSCC mixture optimization.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.8
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• pp.551-560
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• 2004

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed SOFPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional SOFPNNs. The design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial of the consequent part of fuzzy rules, and a collection of the specific subset of input variables) and addresses specific aspects of parametric optimization. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized SOFPNN, the model is experimented with using two time series data(gas furnace and chaotic time series), A comparative analysis reveals that the proposed SOFPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literatures.